Will my Discover Weekly transform into Alex?

My Favourites playlist in Spotify, the playlist of my boyfriend Alex and my discover weekly differ quite a lot. Assuming that discover weekly adapts to what you listen to (the most this article),

I will perform an experiment where I will listen ONLY to my boyfriends playlist for the coming 8 weeks, to see if my Discover Weekly will transform to look lilke my Boyfriends playlist.

The most interesting features to me seem to be Danceability, Energy, Instrumentalness and Tempo.

On the left we see a plot for the Energy, Danceability and Instrumentalness for each playlist, where we use the data of week 1 for discover weekly. The plot wasn’t very interpretable when using all the data from Alex’s and my playlist, as they have so many songs. Thus, I have sampled 500 songs from Alex’s and my Favorites, to make it more clear-cut. Also we can observe that my Favorite’s has some more songs which are less danceable than Alex’s playlist and discover weekly. And that in general, discover weakly has less of a spread for danceability and energy.

The left plot however is hard to interpret, therefore we could look for instance just at the danceability and energy of each playlist. This is what we see on the right. This shows us very clearly that spotify weekly’s data isn’t very spread. For instance, looking at the green line, we can see that the danceability nor the energy go below 0.3 for discover weekly. However, these two plots don’t show us that well how any of the variables are distributed. THus we will look at some histograms per feature.

The Baseline differences

Here we look at some baseline comparisons, as these are important to look at them later on again. For every plot you get a specific feature on the x-axis and the amount on the y-axis. Note that the values of the y-axes differ for the playlists, as discover weekly only has 30 songs and the other 2 have around 3000 songs. These plots clearly show the differences of how the songs are distributed for the different playlists.

For instance, Discover weekly has less energetic songs than the other two. The tempo mostly lies around 125 bmp, whereas the other 2 are more spread.

We now see that only information on the mean of the data, does not give us enough.

The transformation so far?!

This Plot shows us the changes over the 3 weeks for each feature. The values shown are the means of the absolute. The tempo values are devided by 100, else they wouldn’t fit on the plot. Thus tempo= 1.16 equals tempo=116 (am still figuring out how to solve this). The first thing to notice is the big increase in instrumentalness. Whereas the other variables are still fluctuating a lot over time. However, these values only show us the mean values and we have seen before that the distribution of the data might be skewed, therefore we should look at other measures, like the median (I have not had time for this yet, will work on it the coming week).

More plots of mean changes

Another visualisation here showing the Energy in size, the instrumentalness in color and the Danceability on the y-axis. Where the weeks are given as the week number of the year. Where week 6 equals week 1 of this experiment.

Changes of the absolute values

These plot respresent week 1 and week 3 of discover weekly, showing the distribution of all 4 features.

The Chroma of Alex’s least Danceable Song

After doing some research, I found out that Alex’s least danceable song is Lacrimosa, from Amadeus Mozart, which is quite funny as it is one of our favorite classical music songs, which we sing all the time. Looking at the chroma plots, where we can sort of see that this song is played in d minor, which consists of D, E, F, G, A, B♭, and C.Luckily for me, I also have this song in my playlist, a slightly different version from the Choir of King’s College, meaning there might be some more polyphonic parts in this version. However you can clearly see the similarities of the two chroma plots.

Dynamic Time Warping Lacrimosa?

Now we can use Dynamic Time Warping to compute the most optimal alignment between the two features sequences. As hroma norm and distance measure manhattan was chosen, the other options did not seem to work well for this data. We can see the black line diagonally, expressing the optimal warping path.